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Tensorflow

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Tensorflow
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Q1 Keras and the MNIST Dataset (Optional)
Summaries:
The database is also widely used for training and testing in the field of machine learning.
Goal-Using camera we have to recognize the handwritten digit/numbers. The image of
handwritten digit/numbers is the input and number corresponding to image is the output. In
training we are teaching the system that what the number is corresponding to given image. In
testing model we are getting number corresponding to given image. Accuracy is around 95%-
98% means the model is predicting correct number to corresponding image with accuracy 95%
to 98%. There is a construction of model by utilizing high-level Keras API which uses
TensorFlow on the backend. I might want to specify that there are a few high-level TensorFlow
APIs, for example, Layers, Keras, and Estimators which causes us to make neural systems with
high-level information.
Q2: Analyze the MNIST Dataset with Keras
In this case, we first do load the dataset along with the installation of library which the Keras.

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1 Tensorflow Name Date 1 2 Q1 Keras and the MNIST Dataset (Optional) Summaries: The database is also widely used for training and testing in the field of machine learning. Goal-Using camera we have to recognize the handwritten digit/numbers. The image of handwritten digit/numbers is the input and number corresponding to image is the output. In training we are teaching the system that what the number is corresponding to given image. In testing model we are getting number corresponding to given image. Accuracy is around 95%98% means the model is predicting correct number to corresponding image with accuracy 95% to 98%. There is a construction of model by utilizing high-level Keras API which uses TensorFlow on the backend. I might want to specify that there are a few high-level TensorFlow APIs, for example, Layers, Keras, and Estimators which causes us to make neural systems with high-level information. Q2: Analyze the MNIST Dataset with Keras In this case, we first do load the dataset along with the installation of library which the Keras. 2 3 To set up the information we convert the 3-d clusters into frameworks by reshaping width and stature into a solitary measurement. At that point, we convert the gray scale esteems from numbers going between 0 to 255 as shown below. Then we define the data with the application of Keras model and get the output model summary shown below: 3 4 Now, let us app the function of fit() in order to to train the model for 30 epoch, in ...
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